At 50 confidence, participants should be considered high confidence.
Deleting I'M NOT INTERESTED as it is not a concern.
cps['Description'] = cps['Description'].map(str) select = ['branch'] cps = cps[cps['Description'].isin(select)]
77 as 0 23 as 1 24 as 2 65, 66, and 67 as 3, 4, 5 25 as 6 27 as 7 29 as 8 30 as 9 59 (genetic similarity) as 10
select = ['0 Low Scoring_profile', '0 High Scoring_profile', '0 Low confidence Confidence profile', '0 High confident Confidence profile', '0 Non law Legal', '0 Law Legal', '0 Student student', '0 Not student student', '0 Other branch branch', '0 Not a student branch', '0 Law branch branch', '0 Low concern', '0 Medium concern', '0 High concern', '0 High curiosity', '0 Low curiosity', '0 Medium curiosity'] # only keep old and young nndf['Option'] = nndf['Option'].map(str) nndf = nndf[~nndf['Option'].isin(select)]
# # map colours to categories import random # generate random colours amount = len(fif['connections'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
select = ['0 Low Scoring_profile', '0 High Scoring_profile', '0 Older Age Profile', '0 Younger Age Profile', '0 Low confidence Confidence profile', '0 High confident Confidence profile', '0 Non law Legal', '0 Law Legal', '0 Student student', '0 Not student student', '0 Other branch branch', '0 Not a student branch', '0 Law branch branch', '0 Low concern', '0 Medium concern', '0 High concern', '0 High curiosity', '0 Low curiosity', '0 Medium curiosity'] # only keep legal, non legal nndf['Option'] = nndf['Option'].map(str) nndf = nndf[~nndf['Option'].isin(select)]
# map colours to categories import random # generate random colours amount = len(fif['connections'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories fif['connections'] = fif.iloc[:,0].astype(str)+" "+fif.iloc[:,1].astype(str) import random # generate random colours amount = len(fif['connections'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories fif['connections'] = fif.iloc[:,0].astype(str)+" "+fif.iloc[:,1].astype(str) import random # generate random colours amount = len(fif['connections'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
select = ['0 Low Scoring_profile', '0 High Scoring_profile', '0 Low confidence Confidence profile', '0 High confident Confidence profile', '0 Non law Legal', '0 Law Legal', '0 Student student', '0 Not student student', '0 Other branch branch', '0 Not a student branch', '0 Law branch branch', '0 Low concern', '0 Medium concern', '0 High concern', '0 High curiosity', '0 Low curiosity', '0 Medium curiosity'] # only keep old and young
# map colours to categories fif['connections'] = fif.iloc[:,0].astype(str)+" "+fif.iloc[:,1].astype(str) import random # generate random colours amount = len(fif['connections'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
# map colours to categories import random # generate random colours amount = len(npaths['name'].unique()) colour = [] for i in range(0, amount): colour.append("#%06x" % random.randint(i, 0xFFFFFF))
select = ['0 Low Scoring_profile', '0 High Scoring_profile', '0 Older Age Profile', '0 Younger Age Profile', '0 Low confidence Confidence profile', '0 High confident Confidence profile', '0 Non law Legal', '0 Law Legal', '0 Student student', '0 Low concern', '0 Medium concern', '0 High concern', '0 High curiosity', '0 Low curiosity', '0 Medium curiosity'] # only keep branches nndf['Option'] = nndf['Option'].map(str) nndf = nndf[~nndf['Option'].isin(select)]
Something clicked at the word legal in law students :)
77 as 0 23 as 1 24 as 2 65, 66, and 67 as 3, 4, 5 25 as 6 27 as 7 29 as 8 30 as 9
genders = gen_df[['id', 'Option']].copy() genders